Cognitive platform and method for energy management for enterprises

ABSTRACT

This invention is a platform and method for using cognition for managing enterprise energy needs. Cognitive platform allows for dynamic management of energy consumption, demand and baseline calculations by use of a cognitive platform and cognitive device. Employees as well as internal and external stakeholders can set performance indicators and monitor the parameters against the energy performance indicators. Based on the initial knowledge, the system identifies improvements in order to reach the energy key performance indicators. Depending on the feedback, the system learns and improves the accuracy of the predictions and suits them to a given industry or given enterprise scenario. Enterprise-wide energy and/or environmental management covers policies, planning, key performance indicators, goals, targets, works flows, user management, asset mapping, input-output energy flows, conservation options, performance management, analytics. The system and method allow for monitoring and verification by internal and/or external stakeholders.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to Indian Patent Application No:2026/CHE/2014, filed Apr. 21, 2014, the disclosure of which is herebyincorporated by reference in its entirety.

FIELD OF THE INVENTION

This invention relates to dynamic energy management of all energyconsuming equipment, and processes that impact management of energy andenvironmental performance of enterprises. Employees as well as internaland external stakeholders can set performance indicators and monitor theparameters against the energy performance indicators. Based on theinitial knowledge, the system suggests improvement ideas in order toreach the energy key performance indicators. Depending on the feedback,the system learns and improves the accuracy of the predictions and suitsthem to a given industry or given enterprise scenario without much humanintervention. It is a dynamic and self learning system and method thatmanages knowledge based on initial information and subsequent feedbackabout enterprise-wide energy and/or environmental management coveringpolicies, planning, key performance indicators, goals, targets, worksflows, user management, asset mapping, input-output energy flows,conservation options, performance management, analytics. The system andmethod allow for monitoring and verification by internal and/or externalstakeholders.

BACKGROUND OF THE INVENTION

This invention relates to dynamic energy management of all energyconsuming equipment, and process that impact management of energy andenvironmental performance of enterprises using a cognitive platform thathas ability to sense parameters related to energy consumption ofequipment and take decisions based on a rule-set and communicate thechanged or new parameters to equipment to achieve energy efficiency.

Equipment can be any energy consuming equipment such as computer,boiler, furnace, heat exchanger, motor, fan, blower, pump, compressor,heating, ventilation and air-conditioning systems or any otherequipment.

BRIEF DESCRIPTION OF THE PRIOR ART AND PROBLEM TO BE SOLVED

Management of energy and environmental resources by enterprises isemerging as a key consideration for business success. Earlier, energymanagement did not receive much attention as energy costs were not verysignificant and also most equipment were not able to provide thenecessary data that can help in analysis and management. Now enterprisesare focusing on energy consumption and environmental aspects due toincreased costs and awareness of adverse impacts of excessive energyusage and corporate social responsibility.

Today's, energy suppliers and users have expectations on solving thecomplexity of management, scalability, fault tolerant, reliable fastintegration of new technologies as well as attractive business models.

A number of energy management methods and systems are available in themarket today that helps enterprises in energy management. U.S. Pat. No.7,062,389 B2 provides for a system for managing energy consumption byequipment located at site. It provides for methods and systems forgathering consumption data from energy consuming equipment. U.S. Pat.No. 6,178,362 B1 provides for energy management for enterprises thathave widely dispersed energy consuming factories or facilities. U.S.Pat. No. 8,589,112 B2 provides for analyzing and identifying steps forlowering energy consumption in buildings. US Patent application2013/0185120 provides methods and systems for energy benchmarking forgap analysis. It teaches us how to compare different performanceparameters of equipment of one plant against another plant.

These systems are mostly information processing systems based on passiveand structured data. They can process structured data from multiplesources and provide analysis. They can help in periodical usageinformation related to energy consumption, efficiency; and send alarmsto users in case of some boundary conditions are met or exceeded.

However, currently available methods and systems do not take automaticactions to improve energy efficiency, are not able to conduct dynamicsimulation for energy generation & consumption. Specific energyconsumption of equipment and thus of an enterprise depends on manyfactors like user behavior/habits, vendor supplied information,equipment vintage, equipment condition, maintenance quality, weatherfactors, production process, building envelope etc. Currently availablemethods and systems are able to consider only a small set of factors.

Their ability to accurately define/compare peer-groups; conductconsumption/demand analysis; recommend energy enhancement options withcommercially viable return on investment etc., is very limited and theydo not posses the ability to take automatic decisions and actions toimprove energy efficiency.

Real life challenges in energy management are stochastic in nature.Artificial intelligence techniques like machine learning; cognition canhelp in effective energy management. As explained by Nikos Vlasis in thebook “A concise introduction to multi-agent systems and distributedartificial intelligence”, goal for a particular task is the desiredstate of the world. Planning is search through the state space for anoptimal path to the goal. When the world is deterministic, graph searchtechniques can help arrive at the goal. However, in stochastic world,transitions between the states are non-deterministic and hence graphsearch techniques are not useful as uncertainty of transitions need tobe taken into account while planning i.e., searching through the statespace.

Some of the recent research is in the field of use of cognition,advanced statistics and artificial intelligence for energy monitoring.US Patent application 2013/0289788 A1 provides for energy disaggregationtechniques for low resolution whole house energy consumption data wheremethods for creating an appliance signature based upon a low resolutionwhole house profile. European Patent application EP 2 026 299 A1provides for cognitive electric power meter and method for decomposingan input power signal measured at the input power meter into itsconstituent individual loads without incurring home field installationcosts, to allow provision of a detailed usage summary to consumers. USPatent application US 2010/0305889 A1 provides for non-intrusiveappliance load identification using cascaded cognitive learning where bymeasuring an energy consumption signal and using publicly availableinformation of a location, one can estimate probabilities of energizedappliances and further analyzing individual loads and correspondingenergy consumption of appliances.

However these are primarily focused towards gathering information fromenergy consuming equipment using cognition and structured data. They donot address the issues of decision making towards energy efficiency andmanagement of unstructured data. Also, comprehensive energy managementrequires the ability to manage many more parameters than just theequipment. For example, organizational polices, human actions have asignificant impact in specific energy consumption. This also requiresactive management of information related to unstructured data. Currentlyavailable methods and systems are passive in nature and have verylimited or no capability to process unstructured data.

At present there is no comprehensive, intelligent learning systemavailable in the marketplace that can help enterprises manage theirenergy needs efficiently.

One of the key variables for energy efficiency and savings calculationis baseline energy consumption. Traditional systems and methods arecapable of calculating baseline energy consumption based on statisticaltechniques like regression. These techniques take historical data intoconsideration while arriving at baseline energy consumption. Thesesystems and methods assume environmental factors that affect the energyconsumption are deterministic in nature. In real life these factorschange significantly over time.

For example, many enterprises implement various energy efficiencymeasures over a period of time. This brings the need for baseline energyconsumption to involve assumptions about the future as well. An accuratebaseline can be achieved only when baseline calculation is dynamic innature to accommodate dynamic situations especially if the energyservice of the analyzed subject has changed throughout theimplementation of the energy efficiency measure.

An efficient and successful energy management need to include corporatecommitment, appropriate energy management practices/processes promotedthrough energy awareness and training and meaningful metrics fortracking results, maintaining accountabilities and responding in atimely manner to variations etc.

This requires comprehensive end-to-end energy management methods andsystems that are dynamic, intelligent, and self-learning.

With the advancements in technology, today a number of smart meters,smart devices are available that can track the energy consumption andtransmit the information to energy management systems.

Also, with the increased use of software applications and proliferationof mobile networks, devices, software applications, Internet, cloudcomputing, social media applications increasing amounts of data areavailable for processing and decision making.

One of the most important challenges existing today is regarding thedramatic increase in data from various sources like consumption datafrom smart meters, weather and other environmental information fromlocal machines, instruments, local weather stations, production datafrom Manufacturing Resource Planning Information Systems, automationsystems, building occupancy levels, specific planned and unplannedevents, dynamic pricing information from utility suppliers etc. Datafrom various sources need to be analyzed and meaningfully managed forthe effective energy management.

However, present energy management systems being passive in nature, areunable to take advantage of these data as majority of it isunstructured, and dynamic in nature. Dynamic decision-making based oncontinuous learning helps in optimizing energy usage and costs. Newbusiness systems that incorporate cognitive functionality and increaseenergy efficiency through the use of dynamic energy management areneeded.

The preferred embodiments of the present invention overcome the problemsassociated with the existing mechanisms for dynamic energy management byproviding an easily implemented, cost effective, open-standards solutionthat has cognitive capabilities of self learning and decision makingusing statistical, artificial intelligence using initial knowledge andsubsequent feedback.

OBJECTS OF THE INVENTION

The principal object of the present invention is to providecomprehensive dynamic energy management to enterprises, regardless ofwhere the users may be located in the world and regardless of type,location and/or other characteristics of the energy consuming equipmentthat they would like to manage.

Another object of the present invention is to provide a device andmethod that is based on ability to sense parameters related to energyconsumption of equipment, ability to decision making based on a rule-setand ability to communicate the changed or new parameters to equipment.

Another object of the present invention to provide a baseline energyconsumption calculation method and system that is based on historicaldata as well as projected implementation plan of energy saving projectsto enterprises. Baseline energy consumption can be calculated forequipment, facility, plant, department, and enterprise level.

SUMMARY OF THE INVENTION

According to the present invention, there is provided a method formanaging energy in an enterprise, method comprising of

-   -   Accepting, via cognitive platform for energy management, at        least one input parameter related to energy consumption        information of equipment and one energy key performance        indicator related to the equipment, said parameters coming from        a network    -   Comparing, via a cognitive decision maker, the energy        consumption information against energy key performance indicator        and a historical statistics database comprising of historical        information related to energy consumption of the said equipment        and said energy key performance indicator    -   Determining, via cognitive decision maker, a plan that results        in at least one change to the equipment settings to change        energy consumption of the equipment    -   Providing the determined change via a network    -   Updating the historical statistics database with the determined        change in equipment settings

The invention also provides for a system for managing energy in anenterprise, system comprising of

-   -   Accepting, via cognitive platform for energy management, at        least one input parameter related to energy consumption        information of equipment and one energy key performance        indicator related to the equipment, said parameters coming from        a network    -   Comparing, via a cognitive decision maker, the energy        consumption information against energy key performance indicator        and a historical statistics database comprising of historical        information related to energy consumption of the said equipment        and said energy key performance indicator    -   Determining, via cognitive decision maker, a plan that results        in at least one change to the equipment settings to change        energy consumption of the equipment    -   Providing the determined change via a network    -   Updating the historical statistics database with the determined        change in equipment settings

The invention also provides for a device for managing energy consumptionof equipment, device comprising of:

-   -   A sensor that can sense at least one input parameter related to        energy consumption of the equipment    -   An adaptor that has a rule set related to energy consumption    -   A processor that determines at least one change to the equipment        settings based on the rule set    -   An actuator that can provide at least one change or new settings        to the equipment or equipment controller

The invention also provides for logic encoded in non-transitory mediafor execution and when executed by a processor operable to:

-   -   receive sensory input related to energy consumption of an energy        consuming equipment via network    -   process the received input using a cognition based analysis        module comparing the received input with historical statistics        to identify steps to improve energy efficiency    -   communicate at least one of new settings, changed parameters, or        instructions to the energy consuming equipment via network    -   update the historical statistics with at least one of new        settings, changed parameters or instructions related to the        energy consuming equipment.

BRIEF DESCRIPTION OF THE DRAWINGS

To provide a more complete understanding of the present invention andfeatures and advantages thereof, reference is made to the followingdescription taken in conjunction with the accompanying drawings, likereference numerals indicate corresponding parts in the various figures.Elements illustrated in the figures have not necessarily been drawn toscale. For example, the dimensions of some of the elements areexaggerated relative to the other elements. Embodiments incorporatingteachings of the present disclosure are shown and described with respectto the drawings presented herein, in which:

FIG. 1 is an exemplary embodiment of a system utilizing a cognitiveplatform for energy management and cognitive energy device.

FIG. 2 is an exemplary embodiment of databases of cognitive platform forenergy management.

FIG. 3 is an exemplary embodiment of a system utilizing a cognitiveenergy device.

FIG. 4 is a block diagram of an exemplary embodiment of cognitive energydevice connected to energy consuming equipment via equipment controller.

FIG. 5 is a block diagram of another exemplary embodiment of cognitiveenergy device connected to energy consuming equipment.

FIG. 6 is a block diagram of another exemplary embodiment of cognitiveenergy device connected to energy consuming equipment and equipmentcontroller via a network.

FIG. 7 is an exemplary embodiment of a system utilizing a cognitiveenergy device and cognitive platform for energy management.

FIG. 8 is an exemplary embodiment of a system utilizing a cognitiveenergy device and cognitive platform for energy management.

FIG. 9 is an exemplary embodiment of a system utilizing a cognitiveenergy device and cognitive platform for energy management.

FIG. 10 is an exemplary embodiment of a flow chart showing stepsassociated with cognitive decision maker for optimization of energyconsumption.

FIG. 11 is an exemplary embodiment of a flow chart showing stepsassociated with cognitive decision maker for optimization of energydemand.

FIG. 12 is an exemplary embodiment of a flow chart showing stepsassociated with cognitive decision maker for dynamic baselinedefinition.

FIG. 13 is an exemplary embodiment of a flow diagram illustratingcognitive process.

DETAILED DESCRIPTION

While the concepts of the present disclosure are susceptible to variousmodifications and alternative forms, specific embodiments thereof havebeen shown by way of example in the drawings and will herein bedescribed in detail. It should be understood, however, that there is nointent to limit the concepts of the present disclosure to the particularforms disclosed, but on the contrary, the intention is to cover allmodifications, equivalents, and alternatives consistent with the presentdisclosure and the appended claims.

In the following description, numerous specific details such as logicimplementations, types and interrelationships of system components, andlogic partitioning/integration choices are set forth in order to providea more thorough understanding of the present disclosure. One skilled inthe art will appreciate that embodiments of the disclosure may bepracticed without such specific details. In other instances, controlstructures, gate level circuits and full software instruction sequenceshave not been shown in detail in order not to obscure the description ofthe of the concepts described herein.

Those of ordinary skill in the art, with the included descriptions, willbe able to implement appropriate functionality without undueexperimentation.

References in the specification to “one embodiment,” “an embodiment,”“an example embodiment,” etc., indicate that the embodiment describedmay include a particular feature, structure, or characteristic, butevery embodiment may not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, when a particular feature,structure, or characteristic is described in connection with anembodiment, it is submitted that it is within the knowledge of oneskilled in the art to effect such feature, structure, or characteristicin connection with other embodiments whether or not explicitlydescribed.

Embodiments of the concepts described herein may be implemented inhardware, firmware, software, or any combination thereof. Embodiments ofthe concepts described herein may also be implemented as instructionscarried by or stored on one or more machine-readable orcomputer-readable storage media, which may be read and executed by oneor more processors. A machine-readable or computer-readable storagemedium may be embodied as any device, mechanism, or physical structurefor storing or transmitting information in a form readable by a machine(e.g., a computing device). For example, a machine-readable orcomputer-readable storage medium may be embodied as read only memory(ROM) device(s); random access memory (RAM) device(s); magnetic diskstorage media; optical storage media; flash memory devices; mini- ormicro-SD cards, memory sticks, and others.

In the drawings, specific arrangements or orderings of schematicelements, such as those representing devices, modules, instructionblocks and data elements, may be shown for ease of description. However,it should be understood by those skilled in the art that the specificordering or arrangement of the schematic elements in the drawings is notmeant to imply that a particular order or sequence of processing, orseparation of processes, is required. Further, the inclusion of aschematic element in a drawing is not meant to imply that such elementis required in all embodiments or that the features represented by suchelement may not be included in or combined with other elements in someembodiments.

While for the purpose of simplicity, the methodologies are shown anddescribed as a series of acts, it is to be understood and appreciatedthat the methodologies are not limited by the order of acts, as someacts may occur in different orders or concurrently with other acts fromthe shown and described herein. For example, those skilled in the artwill understand and appreciate that a methodology could alternatively berepresented as a series of interrelated states or events, such as in astate diagram. Moreover, not all illustrated acts maybe required toimplement a methodology as described herein.

In general, schematic elements used to represent instruction blocks maybe implemented using any suitable form of machine-readable instruction,such as software or firmware applications, programs, functions, modules,routines, processes, procedures, plug-ins, applets, widgets, codefragments and/or others, and that each such instruction may beimplemented using any suitable programming language, library,application programming interface (API), and/or other softwaredevelopment tools. For example, some embodiments may be implementedusing Java, C++, and/or other programming languages. Similarly,schematic elements used to represent data or information may beimplemented using any suitable electronic arrangement or structure, suchas a register, data store, table, record, array, index, hash, map, tree,list, graph, file (of any file type), folder, directory, database,and/or others.

As described herein, the exemplary embodiments of the invention areapplicable to a system, method, platform and device for use for energymanagement. While various industry terms and acronyms are used, severalterms have the following additional meanings as described.

Example of cognition includes processes by which the sensory inputand/or information is transformed, reduced, elaborated, stored,recovered, used for calculating, reasoning, problem solving, decisionmaking, applying knowledge and/or changing preferences.

Example of a platform includes a system that provides base foradditional endeavors. For example, a platform can allow a variety oftechnologies to merge, allowing for energy management.

Example of a cognitive platform includes any platform that usescognition or machine learning.

Example of a cognitive process includes any process that uses cognition.

Examples of energy sources include electricity, any raw material or fuelthat is in solid, liquid, gaseous or plasma state. Example of energysources also include bi-products, intermediate products generated pre,during or post manufacturing process or during intermediatemanufacturing process.

Example of equipment includes any equipment or resource that consumesenergy.

Example of equipment controllers includes any hardware, and/or hardwarewith software controls equipment, receives and/or sends energy andenvironmental parameters related to the equipment.

Example of a cognitive device includes any device that uses cognition.Example of a cognitive device can be a smart meter, hardware, and/orhardware with software that uses cognition.

Example of a network includes wireless and wire-line telecommunicationnetworks, data networks, computer networks, public or private cloud,local area networks, wide area networks, Internet, content centricnetworks, internet of things or a heterogeneous network, for examplethat can have different telecommunication standards like GSM, CDMA,different versions of same telecommunication standards like GSM 900, GSM1900, different operating systems like Unix, Android, iOS, Windows, ordifferent versions of the same operating system like 2.1, 3.4 ordifferent server hardware like IBM, Lenovo, HP, Oracle, differentdatabases like relational databases, network databases, flat files,object oriented databases, NoSQL databases.

Internet can be any combination of switches, routers, hubs, microwavedevices and other communication equipment that can transfer Internetprotocol messages from one point to another.

Example of a cloud can be any combination of computers connected througha network.

Example of an external application includes any software application orfirmware applications, programs, functions, modules, routines,processes, procedures, plug-ins, application programming interface(API), applets, widgets, code fragments and/or other tool that isconnected with the cognitive platform for energy management eitherdirectly or via any interface or via any other software application.External application may run on same or different operating systems,same or different versions of the same operating system, same ordifferent hardware, and same or different database.

Example of a user application includes any software application orfirmware applications, programs, functions, modules, routines,processes, procedures, plug-ins, application programming interface(API), applets, widgets, code fragments and/or other tool that is usedby people and it is connected with the cognitive platform for energymanagement either directly or via any interface or via any othersoftware application. User application may run on same or differentoperating systems, same or different versions of the same operatingsystem, same or different hardware, and same or different database.

According to the present invention there is provided a cognitiveplatform for energy management that resides at a centralized location,accessible from any location via a network or any other means now knownor later devised. In this exemplary system, enterprises can managevarious aspects of energy efficiently.

FIG. 1 is block diagram of an exemplary embodiment of a system utilizinga cognitive platform for energy management and cognitive energy device.As seen in FIG. 1, users via user applications 4000, externalapplications 3000, cognitive energy devices 2000 connect to cognitiveplatform for energy management 1000 via network 5000. Energy consumingequipment 2270 and/or equipment controllers 2271 connects to thecognitive energy device 2000 via network 5000. Presentation control 1500controls the interaction between cognitive platform for energymanagement 1000 and user applications 4000. Service control 1400controls the interaction between cognitive platform for energymanagement 1000 and external applications 3000 and cognitive energydevice 2000. Data is transmitted between user applications, externalapplications and cognitive energy device and cognitive platform forenergy management 1000 using data receivers and data transmitters 1300.

Data received is interpreted and translated for processing by theprocessor 1100 using data interpretation & translation 1101 and datasent to user applications 4000, external applications 3000 and cognitiveenergy devices 2000 is interpreted and translated using datainterpretation & translation 1101. Cognitive decision maker 1120 andvarious applications 1103 to 1116 process the data for energymanagement.

Decisions made by the cognitive decision maker are communicated to theusers via user applications 4000, external applications 3000 andcognitive energy device 2000. Information processed and output generatedby the applications 1103 to 1116 are communicated to the users via userapplications 4000, external applications 3000 and cognitive energydevice 2000. Middleware 1102 acts as a bridge between variousapplications 1103 to 1116, cognitive decision maker 1120 and datainterpretation & translation 1101. Processed data is stored in database& storage 1200.

In this exemplary embodiment, the architecture is conceptualized forintegration with the existing infrastructure. By adding additionalfunctionality to the existing infrastructure, the energy utilizationwithin a particular facility, plant, area, organization etc., can beoptimized. A combination of cooperative energy saving strategies andcognition enables a reduction in overall energy consumption through theoptimal use of energy resources.

In the exemplary embodiment, the cognitive platform for energymanagement 1000 can be placed physically at a particular facility,plant, geographical area, or organization or as cloud based Software asa Service (SaaS) or in any other manner. Multiple cognitive platformsfor energy management 1000 can be deployed in a hierarchical, ordistributed manner with one or more cognitive platforms for energymanagement 1000 acting as a control node for other cognitive platformsfor energy management 1000 or each cognitive platform for energymanagement acting as a control node for one or more other nodes whileitself acting as a subordinate to another cognitive platform for energymanagement 1000. One or more cognitive platforms for energy management1000 may be connected via a network.

Furthermore, deployment architecture related control and dimensioningrequirements are also considered for achieving large-scale businessneeds of enterprises for energy management.

In the exemplary embodiment, a number of applications 1103 to 1116facilitate in energy management of the enterprise. Structure of theapplications 1103 to 1116 allows definitions and consolidated views,dashboards, drill-downs for easy administration and management atenterprise, department, plant, office, facilities, floor, equipment andasset level within and across enterprises.

Policy & Planner 1103 allows users to define the policies, plans atvarious hierarchical levels within the organization.

KPI & Target Manager 1104 allows users to define periodical keyperformance indicators and targets for energy consumption and demand atvarious hierarchical levels within the organization.

Demand Manager 1105 allows users to manage the sourcing of energy andraw material procurement, vendors based on business plans, policies, andrequirements.

Consumption Manager 1106 allows users to manage the consumption ofenergy and raw material procurement, vendors based on actual consumptioninformation as well as plans and policies.

Process Flows Manager 1107 allows for users to manage energy inflows andoutflows for energy balance within and across energy consumingequipment. Such inflows and outflows and associated wastages can help inidentifying energy losses and facilitate energy efficiency improvementplans and programs.

Assets Manager 1108 allows for users to manage knowledge, informationand data related to all energy consuming assets.

Financials Manager 1109 allows for users to manage knowledge,information and data related to energy financial data. Such data caninclude cost of energy, tariff calculations, cost of raw material,procurement costs, and any other information that impacts the financialaspects of energy consumption or demand.

Efficiency Projects Manager 1110 allows for users to manage knowledge,information and data related to all efficiency improvement projectsundertaken by the enterprise. Such projects can range from simplemaintenance activities of equipment to major changes to the plant andmachinery. Such projects may run for a very short duration (for examplea few minutes to few days) or for a very long duration (say few monthsto several years). Efficiency Projects Manager 1110 allows forsystematic management of all such projects.

Audit Manager 1111 allows for internal and external audits related toenergy management. Users can simulate, emulate, estimate the energyconsumption and demand using data collected during the audit andidentify any improvement areas, problem areas, compliances andnon-compliances.

Training Manager 1112 allows human experts construct the initialknowledge for use by cognitive decision maker 1120. Users can simulate,emulate, estimate, conduct scenario analysis and define goals, tasks,rules, operations, constraints, ends, steps, and algorithms to achievethe goals, tasks. Training Manager 1112 also allows for definingpreferences, priorities and precedence in case of multiple options for agiven goal or task, especially for situations where multi-objectiveconstraint optimization is needed. Training Manager 1112 allows for theenterprise, departments, plants, offices, facilities, floors, andequipment level training.

Reporting & Analytics Tool 1113 allows users and other applications togenerate reports and reports based on analysis. Reporting & AnalyticsTool 1113 allows for the enterprise, departments, plants, offices,facilities, floors, and equipment level reporting.

Work Flow Manager 1114 allows for collaboration between individuals andintra and inter organizational process flows to be managed for effectiveenergy management.

User Manager 1115 allows for management of all users of cognitiveplatform for energy management 1000 based on roles, access rights of theusers.

Admin Governance & Security 1116 manages all security, administrationand governance aspects of the cognitive platform for energy management1000. Admin Governance & Security 1116 can maintain audit trial of everyaction undertaken by users.

Cognitive Decision Maker 1120, Cognitive Process 1121 and CognitiveDecision Maker Logic 1122 are explained in detail after describing FIG.2 for easy understanding.

FIG. 2 is an exemplary embodiment of databases of cognitive platform forenergy management. As seen in FIG. 2, various databases 1211 to 1222form part of the cognitive platform for energy management 1000. Energyhistorical statistics database (EHSD) 1211 is a knowledge repositorythat stores knowledge and information related to all historicalstatistics. EHSD 1211 stores knowledge, information and data relatedrules, skills, data and processes that impact the energy management andfuture decision making. Data collected by the cognitive energy devices2000 is subject to constant changes. Therefore, end-to-end computationsare based not solely on current available operational states, but alsoon associated statistics computed and measured over long time periods,for the particular equipment, facility and/or environment. Historicalstatistical data recorded in the EHSD 1211 enables the decision makeralgorithms to learn from experience and to more accurately predictfuture changes. EHSD 1211 can be seen as time dependent database thatlists the probabilities of different scenarios available for differentassets/equipment at a particular time moments. In addition to the datacollected from all cognitive energy devices 2000, EHSD 1211 also storesand links information from social media, internet databases and otherexternal data sources to get information related to energy andenvironmental parameters, user behaviors, opinions, preferences andother relevant data or information. By increasing the number of sourcesand by increasing the accuracy of identification of energy usage in aparticular situation, a more correct cognitive decision prediction ispossible.

EHSD 1211 stores all information related to active awareness of theequipment information and passive awareness of the surroundingenvironment (structured and unstructured data sourced from own or thirdparties by the enterprise) and associated relationship between thevarious data.

Energy policy KPI database (EPKD) 1212 is a knowledge repository thatstores knowledge, information and data related to enterprise policies,planning, and key performance indicators for energy management. Energyanalytics database (EAD) 1213 is a knowledge repository that storesknowledge, information and data related to various analytics related toenergy management that can be used for reporting purpose. Energy assetsdatabase (EAD) 1214 is a knowledge repository that stores knowledge,information, and data related to enterprise energy assets. Energy tariffdata (ETD) 1215 is a knowledge repository that stores knowledge,information and data related to costs, quantities and other associatedinformation that impacts the cost of energy in an enterprise. Energydynamic data database (EDDD) 1216 is a knowledge repository that storesknowledge, information and data related to periodical energy usage andconsumption information for all energy assets and all other dynamic datais received from cognitive energy device 2000, external applications3000 and user applications 4000. Energy financial info database (EFID)1217 is a knowledge repository that stores knowledge, information anddata related financial information like production data, productionplanning and scheduling data that impacts energy consumption and demand.Energy projects database (EPD) 1218 is a knowledge repository thatstores knowledge, information and data related all energy savingprojects undertaken or planned by the enterprise. Energy reference datadatabase (ERDD) 1219 is a knowledge repository that stores knowledge;information and data related to system and enterprise that impactscognitive platform for energy management 1000. ERDD 1219 also storesknowledge, information and data related to energy process flows withinthe enterprise. Training database (TD) 1220 is a knowledge repositorythat stores knowledge, information and data related to training ofvarious uses of the cognitive platform for energy management 1000 andcognitive energy device 2000. Audit database (AD) 1221 is a knowledgerepository that stores knowledge, information and data related tointernal and external energy audits conducted by the enterprise.

CPEM admin database (CAD) 1222 is a knowledge repository that storesknowledge, information and data related to administration, governance,security, workflows and users.

Various databases 1211 to 1222 in the exemplary embodiment can be onsame or different operating systems like Unix, Android, iOS, Windows;same or different versions of the same operating system like 2.1, 3.4;or same or different server hardware like IBM, Lenevo, HP, Oracle; orsame or different databases like relational databases, networkdatabases, flat files, object oriented databases, NoSQL databases. Oneor more of the databases 1211 to 1222 can be combined into a singledatabase. One or more databases 1211 to 1222 can be on a network.Databases 1211 to 1222 can be deployed in single tier or multiple tierswith or without redundancy, availability, fail-safe options with singleor multi phase commits.

Cognitive decision maker 1120 has ability to improve performance throughlearning. Artificial intelligence techniques enable such changedbehavior. It uses cognitive process 1121 that perceives and acts.Architecture and configuration of cognitive process 1121 depends on thebusiness requirements. Cognitive process 1121 for simple awareapplication requirement maps percepts to actions. Cognitive process 1121for an adaptive application includes a state memory and achieves moresophisticated behavior. Cognitive process 1121 for goal-based analysishas a model of the environment and equipment and can estimate theresults of alternatives. Cognitive process 1121 supports learning withfeedback, artificial neural networks, metaheuristic algorithms, hiddenMarkov model, rule based systems, ontology-based and case based systems.

Goal information identifies states that are desirable. It also maintainssteps required to reach the goal. Steps can be a single step to reachthe goal or a sequence of steps to reach the goal. Cognitive process1121 may also maintain feedback information. Cognitive decision maker1120 uses knowledge, data, and information stored in database & storage1200.

Cognitive process 1121 can include a variety of artificial intelligencetechniques to realize the capability. Standard searching solutions likebreadth-first search, depth limited search, iterative deepening depthfirst algorithms can yield different performance, time or spacecomplexity as a function of environment. Genetic algorithms can be usedto explore the action space in a controlled manner. Neural engineeringtechniques can be used to explore the possible relationships betweenpercepts and actions. Neural network is initially trained by providing aset of known inputs and desired outputs based on information availablein the databases EAID 1214 and ERDD 1219.

The neural network, to learn the desired behaviors, uses theseobservations. Feedback provided by the desired output is used forlearning using various techniques like inductive learning, ensemblelearning. Any fielded cognitive process will have an initial set ofknowledge based on EAID 1214 and ERDD 1219. Statistical models oflearning include Bayesian computations of probability as a function ofpercepts, self-organizing maps such as Kohonen networks, Backpropagation neural networks or any other model. A feedback mechanismdetermines when the candidate rules are allowed to survive or if theyare to be removed. Cognitive process can use the initial knowledgesystem constructed by the human expert using training manager 1112.Cognitive process 1121 takes decision on how to achieve a goal by takingspecific objectives and constraints into consideration. There can bemultiple cognitive processes running simultaneously on various preceptsat a given point in time.

Cognitive process 1121 obtains data related to goals, plans, rules,operations constraints, ends from EHSD 1211 and current state fromrelevant data source. For example current state for energy consumptiondata can be from EDDD 1216, energy demand data, baseline information canbe from any one or more of EAD 1213, EAID 1214, ERDD 1219, and EFID1217, KPIs can be from EPKD 1212. Cognitive process 1121 also obtainsknowledge and information related to problem solving methods that impactcognitive function, specialized skills like perceptive, reactive, actionrecognition and rule based action executing from EHSD 1211, algorithmicoptimization inputs from EHSD 1211. Cognitive process 1121 applies theknowledge on the data and information and arrives at the possibleoptions to achieve the goal with weights for each possible option.

Cognitive decision maker logic 1122 supports multi-objective constraintoptimization based on multiple cognitive processes 1121 thus ensuringthe optimal solution taking all goals, objectives and constraints intoconsideration.

FIG. 3 is an exemplary embodiment of a system utilizing a cognitiveenergy device. As seen in FIG. 3, cognitive energy device 2000 isconnected to cognitive platform for energy management 1000 via network5000. Energy consuming equipment 2270 or equipment controller 2271 ofenergy consuming equipment 2270 is connected with the cognitive energydevice 2000. Sensor 2040 of the cognitive energy device 2000 senses theequipment's energy consumption and other parameters and communicates thesensed information to the processor 2030. Memory 2060 of the cognitiveenergy device 2000 contains information related to the equipment energyconsumption and control information. Adaptor 2020 contains specificskill, process, knowledge, and information relevant to the equipment2270 or equipment controller 2271 that the cognitive energy device 1000is controlling. Processor 2030 determines whether any instructionsshould be given to the equipment 2270 or equipment controller 2271 whichin turn can control the equipment 2270 and communicates thoseinstructions via actuator 2050.

Sensed information is also transmitted to cognitive platform for energymanagement 1000 via the network 5000. Communication protocol & medium2010 handles specific message types formatted to handle all necessarydata exchange with the cognitive platform for energy management 1000.The cognitive platform for energy management 1000 can also communicate adecision to the cognitive energy device 2000 to implement changes to theequipment settings or equipment controller and cognitive energy deviceexecutes such instructions using actuator 2050.

In the exemplary embodiment, cognitive devices are intelligent devicesthat can be programmed and configured dynamically. Different parameterscan be configured on the fly, in near real-time depending on theenvironment and avoiding bottlenecks. Cognitive energy device is acognitive device. It is software controlled and has reconfigurableinterface where the physical layer (hardware) behavior can besignificantly changed as a consequence of changes in the software i.e.,the same hardware entity can perform different functions at differenttimes. The main advantage of this software controlled cognitive energydevice are in terms of multi-functionality e.g., compactness, powerefficiency, ease of upgrading and ability to handle multiple standards.

FIG. 4 is a block diagram of an exemplary embodiment of cognitive energydevice 2000 connected to energy consuming equipment 2270 via equipmentcontroller 2271. Equipment controller 2271 receives relevant informationfrom equipment 2270 and communicates the same to cognitive energy device2000. Equipment controller 2271 also receives instructions fromcognitive energy device 2000 and executes the same onto equipment 2270.

FIG. 5 is a block diagram of another exemplary embodiment of cognitiveenergy device 2000 connected to energy consuming equipment 2270directly. Cognitive energy device 2000 receives relevant informationrelated to energy consumption from the equipment 2270. Cognitive energydevice 2000 sends instructions to equipment 2270 as appropriate.

FIG. 6 is a block diagram of another exemplary embodiment of cognitiveenergy device 2000 connected to energy consuming equipment 2270 andequipment controller 2271 via a network 5000. Cognitive energy devicereceives information and sends instructions to equipment 2270 andequipment controller 2271 as appropriate.

FIG. 7 is an exemplary embodiment of a system utilizing a cognitiveenergy device and cognitive platform for energy management. As seen inFIG. 7, cognitive energy device 2000 is connected to energy consumingequipment 2270, equipment controller 2271 and cognitive platform forenergy management 1000 via a network 5000. Cognitive energy devicereceives information and sends instructions to equipment and equipmentcontroller as appropriate. Cognitive platform for energy management 1000receives and sends information from/to cognitive energy device 2000 andthus controls the energy consuming equipment 2270 over network 5000.

FIG. 8 is an exemplary embodiment of a system utilizing a cognitiveenergy device and cognitive platform for energy management. As seen inFIG. 8, cognitive energy device 2000 is connected to energy consumingequipment 2270, equipment controller 2271 and cognitive platform forenergy management 1000 via a network 5000. External applications 3000are connected to the cognitive platform for energy management. Variousexemplary energy consuming equipment 2270 indicated include computers,boiler, furnace, heat exchanger, motor, fan/blower, pump, compressor.HVAC system and lighting are connected to the equipment controllerbuilding management system 2271. Cognitive energy device receivesinformation and sends instructions to equipment and equipment controlleras appropriate. Cognitive platform for energy management 1000 receivesand sends information from/to cognitive energy device 2000 and thuscontrols the energy consuming equipment 2270 over network 5000. If anyof the equipment 2270 or equipment controllers 2271 can not be connectedto the network, the relevant input information for the cognitiveplatform for energy management 1000 can be manually captured and datacan be manually inputted by the users and similarly the relevant outputinformation from the cognitive platform for energy management 1000 canbe used by the users for manually adjusting the relevant equipment 2270or equipment controllers 2271.

FIG. 9 is an exemplary embodiment of a system utilizing a cognitiveenergy device and cognitive platform for energy management. As seen inFIG. 9, cognitive energy device 2000 is connected to energy consumingequipment 2270, equipment controller 2271, external applications 3000and cognitive platform for energy management 1000 via a network 5000.Various exemplary energy consuming equipment 2270 indicated includecomputer, communication equipment, boiler, furnace, heat exchanger,motor, fan/blower, pump, compressor. HVAC system and lighting areconnected to the equipment controller building management system 2271.Cognitive energy device receives information and sends instructions toequipment and equipment controller as appropriate. External applications3000 are connected to the cognitive platform via network 5000 for energymanagement. Cognitive platform for energy management 1000 receives andsends information from/to cognitive energy device 2000 and thus controlsthe energy consuming equipment 2270 over network 5000. If any of theequipment 2270 or equipment controllers 2271 can not be connected to thenetwork, the relevant input information for the cognitive platform forenergy management 1000 can be manually captured and data can be manuallyinputted by the users and similarly the relevant output information fromthe cognitive platform for energy management 1000 can be used by theusers for manually adjusting the relevant equipment 2270 or equipmentcontrollers 2271.

FIG. 10 is an exemplary embodiment of a flow chart illustrating anexample step(s) associated with cognitive decision maker 1120 foroptimization of energy consumption. While, for the purposes ofsimplicity of explanation, the methodology is shown and described as aseries of acts, it is to be understood and appreciated that themethodologies are not limited by the order of acts, as some acts mayoccur in different orders or concurrently with other acts from the shownand described herein. For example, those skilled in the art willunderstand and appreciate that a methodology could alternatively berepresented as a series of interrelated states or events, such as in astate diagram. Moreover, not all illustrated acts maybe required toimplement a methodology as described herein.

As seen in FIG. 10, cognitive decision maker 1120 obtains consumptiondata from EDDD 1216. Cognitive decision maker 1120 then obtains therelevant data like key performance indicators (KPI), and targets fromEPKD 1212. Cognitive decision maker 1120, then compares whether theconsumption data is within the KPI or target. If the consumption iswithin the KPI or target, cognitive decision maker updates the EHSD 1211with relevant information. If consumption is not within the KPI ortarget, then cognitive decision maker 1120 obtains information relatedto specialized knowledge related to operations and associatedconstraints from various other data bases. For example, cognitivedecision maker 1120 obtains information related to the specific energyconsuming equipment from EAID 1214. Such information can includestandard energy consumption norms defined by the vendor of theequipment, specific operating conditions like operating temperature,humidity, and other information that may impact energy consumption.Cognitive decision maker 1120 obtains financial information relatedenergy consuming equipment from EFID 1212, information related tospecific energy projects undertaken by the enterprise from EPD 1218 forthe specific equipment or facility, and tariff related information fromETDD 1215. Cognitive decision maker 1120, then obtains the relevantprior knowledge EHSD 1211. Cognitive decision maker 1120, then analyzescurrent scenario against prior knowledge and predicts appropriate actionfor energy consumption optimization. The cognitive decision maker 1120predicts one or more appropriate actions and options for actions. Ifmore than option exists for optimization, cognitive decision maker 1120,then decides which one of those options is best suited for the givenscenario based on criteria like weightage score, algorithms, customerpreferences as defined in ERDD 1219 and CAD 1222. Cognitive decisionmaker 1120, then communicates the best suited option to datainterpretation & translation 1101 for further processing andcommunicating the changed settings or new settings to the equipment 2070and/or equipment controller 2071. Cognitive decision maker 1120 alsoupdates the EHSD 1211.

FIG. 11 is an exemplary embodiment of a flow chart illustrating anexample step(s) associated with cognitive decision maker 1120 foroptimization of energy demand. As seen in FIG. 11, cognitive decisionmaker 1120 obtains energy demand data from EAD 1213. Cognitive decisionmaker 1120 obtains the relevant data like key performance indicators(KPI) and targets from EPKD 1212. Cognitive decision maker 1120,compares whether the energy demand is within the KPI or target. If thedemand is within the KPI or target, cognitive decision maker updates theEHSD 1211 with relevant information. If demand is not within the KPI ortarget, then cognitive decision maker 1120 obtains information relatedto specialized knowledge related to operations and associatedconstraints from various other data bases. For example, cognitivedecision maker 1120 obtains information related to the specific energyconsuming equipment from EAID 1214. Such information can includestandard energy consumption norms defined by the vendor of theequipment, specific operating conditions like operating temperature,humidity, and other information that may impact energy consumption.Cognitive decision maker 1120 obtains financial information relatedenergy consuming equipment from EFID 1212, information related tospecific energy projects undertaken by the enterprise from EPD 1218 forthe specific equipment or facility, and tariff related information fromETDD 1215. Cognitive decision maker 1120 obtains the relevant priorknowledge EHSD 1211. Cognitive decision maker 1120 analyzes currentscenario against prior knowledge and predicts appropriate action forenergy demand optimization. The cognitive decision maker 1120 predictsone or more appropriate actions and options for actions. If more thanoption exists for optimization, cognitive decision maker 1120, thendecides which one of those options is best suited for the given scenariobased on criteria like weightage score, algorithms, customer preferencesas defined in ERDD 1219 and CAD 1222. Cognitive decision maker 1120,then communicates the best suited option to data interpretation &translation 1101 for further processing and communicating the changedsettings or new settings to the equipment 2070 and/or equipmentcontroller 2071. Cognitive decision maker 1120 also updates the EHSD1211.

FIG. 12 is an exemplary embodiment of a flow chart illustrating anexample step(s) associated with cognitive decision maker 1120 fordynamic baseline definition. As seen in FIG. 12, cognitive decisionmaker 1120 obtains production data, requirements from EFID and/orexternal applications for specific equipment, facility, or zone etc.Cognitive decision maker 1120 obtains historical consumption knowledgefrom EHSD 1211, standard specifications and energy consumption normsfrom EAID 1214, ERDD 1219, and external applications 3000. Cognitivedecision maker 1120 also obtains information related to policies, keyperformance indicators (KPIs) from EPKD 1212. Cognitive decision maker1120 then verifies whether a baseline already exists for the specificequipment, facility, or zone etc.

If the baseline is not available, then cognitive decision makernormalizes the data and calculates the baseline for the equipment,facility or zone etc. Cognitive decision maker 1120 defines the baselinefor the specific equipment, facility of zone etc and updates EHSD 1211with the relevant information.

If a baseline is already existing, cognitive decision maker verifieswhether variables that influence the baseline have changed or not. Ifthere is no change to the influencing variables, cognitive decisionmaker 1120 updates the EHSD 1211 with the relevant information.

If the influencing variables have changed, then cognitive decision maker1120 identifies options for adjusting the variables, selects an optionbased on criteria like weightage scores, algorithms, customerpreferences as defined in ERDD 1219 and CAD 1222 and then adjusts thevariables based on the selected option. Cognitive decision maker 1120updates the EHSD 1211 with the relevant information.

FIG. 13 is an exemplary embodiment of a flow diagram illustratingcognitive process. As seen in FIG. 13, cognitive process 1121 analysesthe current status, state/desirability and arrives at possible actions Aand B. It executes action A and re-analyses the current status,state/desirability and arrives at possible action D. It executes actionD and re-analyses the current status, state/desirability and arrives atpossible action G. It executes action G and re-analyses the currentstatus, state/desirability and arrives at conclusion that there is nopossible action available from the prior-knowledge.

Cognitive process rolls back to original status, state/desirability andexecutes action B and re-analyses the current status, state/desirabilityand arrives at possible actions C and E. It executes action E andre-analyses the current status, state/desirability and arrives atconclusion that there is no possible action available from theprior-knowledge. Cognitive process rolls back and executes action C andre-analyses the current state, state/desirability and arrives atpossible action F. If executes action F, re-analyses the current state,state/desirability and arrives at the conclusion that it is the goalstate (desirable state). Accordingly, cognitive process 1121 concludesthat actions B, C, F are the right steps to be conducted in thatparticular order to achieve the desired goal.

We claim:
 1. A method for managing energy in an enterprise, comprising:a. accepting, via a cognitive platform for energy management, at leastone parameter related to a current state of equipment within anenterprise and at least one parameter related to at least one desirablestate of an enterprise; b. analyzing, via a cognitive decision maker,and identifying one or more sets of hierarchies of possible actions toreach one or more sets of hierarchies of possible desirable states of anenterprise wherein the one or more sets of hierarchies of possibledesirable states of the enterprise are selected from the groupconsisting of a local state related to equipment and a global staterelated to enterprise, and choosing a possible set of actions based onthe hierarchy of desirable local and global states of the enterprise; c.implementing the chosen set of actions, re-analyzing the current stateof equipment and verifying whether it approaches the desirable local andglobal states of enterprise and rolling-back local desirable states ofequipment if global states of enterprise are not approached; d.repeating steps b and c until there is at least one set of hierarchiesof possible actions that can achieve the desirable state of globalstates of enterprise or until all possible sets of identified hierarchyof desirable local and global states of enterprise are exhausted; e.updating the historical statistics database with one set of parametersrelated to local hierarchies of possible actions that can achieve thedesirable global states of enterprise.
 2. A method as claimed in claim1, wherein one or more sets of hierarchies of possible actions are basedon multi-objective constraint optimization.
 3. A method as claimed inclaim 1, wherein said analyzing is based on any one of rule basedaction, algorithmic optimization, perceptive action or reactive action.4. A method as claimed in claim 1, wherein the cognitive decision makeruses the parameters related to hierarchies of possible actions asobservation to learn the desired behavior.
 5. A system for managingenergy in an enterprise, the system comprising: a. a module foraccepting, via a cognitive platform for energy management, at least oneparameter related to current state of equipment within an enterprise andat least one parameter related to at least one desirable state of anenterprise; b. a module for analyzing, via a cognitive decision maker,and identifying one or more sets of hierarchies of possible actions toreach one or more sets of hierarchies of possible desirable states of anenterprise wherein the one or more sets of hierarchies of possibledesirable states of the enterprise are selected from the groupconsisting of a local state related to equipment and a global staterelated to enterprise and choosing a possible set of actions based onthe hierarchy of desirable local and global states of the enterprise; c.a module for implementing the chosen set of actions and re-analyzing thecurrent state of equipment and verifying whether it approaches thedesirable local and global states of enterprise and rolls-back localdesirable states of equipment if global states of enterprise are notapproached; d. a module for repeating steps b and c above until there isat least one set of hierarchies of possible actions that can achieve thedesirable state of global states of enterprise or until all possiblesets of identified hierarchy of desirable local and global states ofenterprise are exhausted; e. a module for updating a historicalstatistics database with one set of parameters related to localhierarchies of possible actions that can achieve the desirable globalstates of enterprise.
 6. A system as claimed in claim 5, wherein the oneor more sets of hierarchies of possible actions are based onmulti-objective constraint optimization.
 7. A system as claimed in claim5, wherein the analyzing is based on any one of rule based action,algorithmic optimization, perceptive action or reactive action.
 8. Asystem as claimed in claim 5, wherein the cognitive decision maker usesthe parameters related to hierarchies of possible actions as observationto learn the desired behavior.
 9. A device for managing energy of anenterprise, comprising: a. a sensor that can sense at least one inputparameter related to current state of equipment b. an adaptor that has arule set related to energy consumption one or more sets of hierarchiesof possible actions related to equipment to reach one or more sets ofhierarchies of possible desirable states of an enterprise wherein theone or more sets of hierarchies of possible desirable states of theenterprise are selected from the group consisting of a local staterelated to equipment and a global state related to the enterprise c. aprocessor that determines at least one possible set of actions based onthe hierarchy of desirable local state and global state; d. an actuatorthat can implement the determined set of actions or roll-back as andwhen needed.
 10. A device as claimed in claim 9, wherein the set ofhierarchies of possible actions is configured via a network.
 11. Adevice as claimed in claim 9, wherein the set of hierarchies of possibleactions is based on an algorithm.
 12. A device as claimed in claim 9 orclaim 10 or claim 11, wherein the set of hierarchies of possible actionsis based on information obtained from a cognitive platform.
 13. A deviceas claimed in claim 9 wherein the sensor senses the current state ofequipment and actuator implements the chosen set of actions or roll-backas needed to equipment via an equipment controller.
 14. A non-transitorycomputer-readable medium comprising logic, when executed by a processoroperable to: a. receive sensory input related to current state of anenergy consuming equipment, b. analyze using a cognition based analysismodule and identify one or more sets of hierarchies of possible actionsto reach one or more sets of hierarchies of possible desirable states ofan enterprise wherein the one or more sets of hierarchies of possibledesirable states of the enterprise are selected from the groupconsisting of a local state related to equipment and a global staterelated to enterprise and choose possible set of actions based on thehierarchy of desirable local and global states of the enterprise; c.implement the chosen set of actions and re-analyze the current state ofequipment and verify whether it approaches the local and global statesof enterprise and rolling-back desirable states of equipment if globalstates of enterprise are not approached; d. repeat steps b and c aboveuntil there is at least one set of hierarchies of possible actions thatcan achieve the desirable global states of enterprise or until allpossible sets of identified hierarchy of desirable local and globalstates of enterprise are exhausted; e. updating a historical statisticsdatabase with the one set of parameters related to hierarchies ofpossible actions related to equipment that can achieve the desirablestate of enterprise.